Finetuning experiments
Collection
Using common-known datasets to finetune several models. Experiments are run in Colab.
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6 items
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Updated
finetuned-DebertaV3-imdb-TextClassification
This model is a fine-tuned version of microsoft/deberta-v3-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1329
- Accuracy: 0.9631
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.1543 | 1.0 | 1042 | 0.1108 | 0.9592 |
| 0.0929 | 2.0 | 2084 | 0.1329 | 0.9631 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for JinbiaoZhu/finetuned-DebertaV3-imdb-TextClassification
Base model
microsoft/deberta-v3-base